Data Science and Analytics Tools - Web Scraping for Market Sentiment Analysis

 Notes:

• What problem does it solve?
  Scrapes online market data (e.g., news, forums, social media) to analyze customer sentiment about products or services.

• How can businesses or users benefit from customizing the code?
  They can track competitor products, monitor brand reputation, and gain insights into market trends based on customer feedback.

• How can businesses or users adopt the solution further, if needed?
  Can be automated to run periodically, scraping fresh data and generating sentiment reports in real time.

Actual Python Code:

import requests

from bs4 import BeautifulSoup

from textblob import TextBlob

# URL to scrape (e.g., product reviews or social media posts)

url = 'https://example.com/product-reviews'

# Send HTTP request and get page content

response = requests.get(url)

soup = BeautifulSoup(response.text, 'html.parser')

# Extract text from the page (assumed to be reviews)

reviews = soup.find_all('div', class_='review-text')

# Analyze sentiment of each review

sentiments = []

for review in reviews:

    text = review.get_text()

    blob = TextBlob(text)

    sentiment = blob.sentiment.polarity

    sentiments.append(sentiment)

# Calculate average sentiment

avg_sentiment = sum(sentiments) / len(sentiments)

print(f'Average Sentiment: {avg_sentiment}')